آمار
| تعداد نشریات | 36 |
| تعداد شماره‌ها | 1,209 |
| تعداد مقالات | 8,770 |
| تعداد مشاهده مقاله | 7,367,242 |
| تعداد دریافت فایل اصل مقاله | 4,314,189 |
یک روش جدید برای نهان‌نگاری تصاویر به کمک تبدیلات موجک | ||
| پدافند الکترونیکی و سایبری | ||
| مقاله 8، دوره 7، شماره 3 - شماره پیاپی 23، آبان 1398، صفحه 83-91 اصل مقاله (1.06 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| محسن شاهرضایی* 1؛ نوید رزمجوی2 | ||
| 1دانشگاه امام حسین(ع) | ||
| 2کارشناسی ارشد دانشگاه امام حسین | ||
| تاریخ دریافت: 10 آذر 1397، تاریخ بازنگری: 02 بهمن 1397، تاریخ پذیرش: 14 اسفند 1397 | ||
| چکیده | ||
| در عصر حاضر، به موازات گسترش فناوری در زمینههای مختلف و امکان شدن انجام کارهای مختلف از راه دور و با استفاده از شبکه‌های داخلی و اینترنت، مشکلاتی نیز کنار این فناوریها بهوجود آمده است. از جمله این مشکلات میتوان به حفظ امنیت اطلاعات در زمان ارسال و دریافت برای جلوگیری از دسترسیهای غیرمجاز اشاره کرد. پنهاننگاری، علم پنهانسازی و پوشاندن اطلاعات مورد نظر با بیشترین میزان دقت به امنیت، جهت انتقال امن اطلاعات بین نقاط موردنظر است، به طوری که حتی در صورت دستیابی افراد غیرمجاز به اطلاعات، امکان دستیابی به داده‌های پنهان وجود نخواهد داشت. در پنهاننگاری دو ویژگی مهم وجود دارد. اول اینکه تعبیه اطلاعات نباید تغییرات قابل توجهی در محیط میزبان ایجاد کند و دوم اینکه خواص آماری پوشانه و پیام تا حد امکان به هم نزدیک باشند تا عمل پنهاننگاری بهتر انجام گیرند. هدف اصلی در این مقاله ارائه یک روش جدید بر اساس تبدیلات موجک برای رسیدن به یک روش پنهاننگاری مناسب است. در این روش، بر اساس تبدیلات موجک، تصاویر رنگی موردنظر برای پنهاننگاری در تصاویر دیگری نهان‌نگاری میشوند که بهصورت عادی قابل مشاهده نیستند و برای مشاهده آن به کلید آن نیاز خواهد بود. نتایج شبیهسازی نشاندهنده کارایی بالای روش پیشنهادی است. | ||
| کلیدواژه‌ها | ||
| احراز هویت؛ نهان‌نگاری؛ تصویر دیجیتال؛ تبدیل موجک؛ روش تجزیه مقدار تکین | ||
| عنوان مقاله [English] | ||
| A New Method for Image Steganography Using Discrete Wavelet Transforms | ||
| نویسندگان [English] | ||
| M. Shahrezaei1؛ N. Razmjoei2 | ||
| 2- | ||
| چکیده [English] | ||
| In the present era, along with the spread of technology in different fields and the possibility of remotely accomplishing different tasks and using internal networks and the Internet, related problems and challenges have occurred. One of these problems is maintaining the security of information while sending and receiving, to prevent unauthorized access. Watermarking is the science of hiding and covering the information with the highest degree of accuracy in security, in order to securely transfer information between the points of interest, so that even when an unauthorized access happens, there is no access to the watermarked data. There are two important features in watermarking. First, the information embedding should not make significant changes in the host environment, and second, the statistical properties of the cover image and the message should be as close as possible in order to have better cryptography. The main purpose of this paper is to provide a new method based on discrete wavelet transform to achieve a suitable cryptographic method. In this method, based on wavelet transforms, color images are watermarked so that they are not normally visible and their key is needed to view them. Simulation results indicate the efficiency of the proposed method. | ||
| کلیدواژه‌ها [English] | ||
| Authentication, cryptography, digital image, wavelet transform, singular value decomposition | ||
| مراجع | ||
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[1] M. Ghayoori Sales, Gh. R. Bazdar, and A. Sarkardei, “Introduction of the Entropy-Based Method for Finding Influential Nodes in Information Dissemination on Online Social Networks,” Journal of Electronical & Cyber Defence, vol. 6, pp. 1-9, 2018.## [2] A. A. P. K. V. Hosseinnezhad, “Bayesian Networks Based Trust Model in Social Networks,” Journal of Electronical & Cyber Defence, vol. 6, p. 10, 2018.## [3] N. Jiang, N. Zhao, and L. Wang, “LSB based quantum image steganography algorithm,” International Journal of Theoretical Physics, vol. 55, pp. 107-123, 2016.## [4] M. M. Hashim, M. S. M. Rahim, F. A. Johi, M. S. Taha, and H. S. Hamad, “Performance evaluation measurement of image steganography techniques with analysis of LSB based on variation image formats”, International Journal of Engineering & Technology, vol. 7, pp. 3505-3514, 2018.## [5] F. Ahmed and I. S. Moskowitz, “Correlation-based watermarking method for image authentication applications,” Optical Engineering, vol. 43, pp. 1833-1839, 2004.## [6] S. A. Parah, J. A. Sheikh, N. A. Loan, F. Ahad, and G. M. Bhat, “Utilizing neighborhood coefficient correlation: a new image watermarking technique robust to singular and hybrid attacks,” Multidimensional Systems and Signal Processing, vol. 29, pp. 1095-1117, 2018.## [7] K. Chaitanya and K. G. Rao, “A Novel Approach To Medical Image Watermarking for Tamper Detection and Rcovery of Region of Interest using Predictive Coding and Hashing,” Journal of Theoretical & Applied Information Technology, vol. 96, 2018.## [8] W. C. Chu, “DCT-based image watermarking using subsampling,” IEEE transactions on multimedia, vol. 5, pp. 34-38, 2003.## [9] M. Singh and A. Saxena, “Image watermarking using discrete cosine transform [DCT] and genetic algorithm [GA],” 2017.## [10] N. Kashyap and G. Sinha, “Image watermarking using 3-level discrete wavelet transform (DWT),” International Journal of Modern Education and Computer Science, vol. 4, p. 50, 2012.## [11] S. P. Ambadekar, J. Jain, and J. Khanapuri, “Digital Image Watermarking Through Encryption and DWT for Copyright Protection,” in Recent Trends in Signal and Image Processing, ed: Springer, pp. 187-195, 2019.## [12] M.-J. Hwang, J. Lee, M. Lee, and H.-G. Kang, “SVD-based adaptive QIM watermarking on stereo audio signals,” IEEE Transactions on Multimedia, vol. 20, pp. 45-54, 2018.## [13] M. Kaur and V. K. Attri, “A Survey on Digital Image Watermarking and Its Techniques,” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 8, pp. 145-150, 2015.## [14] T. K. Araghi, A. B. A. Manaf, M. Zamani, and S. K. Araghi, “A survey on digital image watermarking techniques in spatial and transform domains,” Int. J. Adv. Image Process. Techn.–IJIPT, vol. 3, pp. 6-10, 2016.## [15] C. Kumar, A. K. Singh, and P. Kumar, “A recent survey on image watermarking techniques and its application in e-governance,” Multimedia Tools and Applications, vol. 77, pp. 3597-3622, 2018.## [16] E. Ganic and A. M. Eskicioglu, “Robust DWT-SVD domain image watermarking: embedding data in all frequencies,” in Proceedings of the 2004 Workshop on Multimedia and Security, pp. 166-174, 2004.## [17] D. Kahaner, C. Moler, and S. Nash, “Numerical methods and software,” Englewood Cliffs: Prentice Hall, 1989.## [18] H. Andrews and C. Patterson, “Singular value decomposition (SVD) image coding,” IEEE transactions on Communications, vol. 24, pp. 425-432, 1976.## [19] A. M. Rufai, G. Anbarjafari, and H. Demirel, “Lossy image compression using singular value decomposition and wavelet difference reduction,” Digital signal processing, vol. 24, pp. 117-123, 2014.## [20] J. C. S. de Souza, T. M. L. Assis, and B. C. Pal, “Data compression in smart distribution systems via singular value decomposition,” IEEE Transactions on Smart Grid, vol. 8, pp. 275-284, 2017.## [21] J. SairaBanu, R. Babu, and R. Pandey, “Parallel implementation of Singular Value Decomposition (SVD) in image compression using open Mp and sparse matrix representation,” Indian Journal of Science and Technology, vol. 8, 2015.## [22] N. M. Makbol, B. E. Khoo, and T. H. Rassem, “Block-based discrete wavelet transform-singular value decomposition image watermarking scheme using human visual system characteristics,” IET Image Processing, vol. 10, pp. 34-52, 2016.## [23] N. M. Makbol and B. E. Khoo, “A new robust and secure digital image watermarking scheme based on the integer wavelet transform and singular value decomposition,” Digital Signal Processing, vol. 33, pp. 134-147, 2014.## [24] M. Ali, C. W. Ahn, and M. Pant, “A robust image watermarking technique using SVD and differential evolution in DCT domain,” Optik-International Journal for Light and Electron Optics, vol. 125, pp. 428-434, 2014.## [25] A. Cichocki, D. Mandic, L. De Lathauwer, G. Zhou, Q. Zhao, C. Caiafa, et al., “Tensor decompositions for signal processing applications: From two-way to multiway component analysis,” IEEE Signal Processing Magazine, vol. 32, pp. 145-163, 2015.## [26] S. Chakraborty, S. Chatterjee, N. Dey, A. S. Ashour, and A. E. Hassanien, “Comparative approach between singular value decomposition and randomized singular value decomposition-based watermarking,” in Intelligent techniques in signal processing for multimedia security, ed: Springer, pp. 133-149, 2017.## [27] F. Fioranelli, M. Ritchie, and H. Griffiths, “Classification of unarmed/armed personnel using the NetRAD multistatic radar for micro-Doppler and singular value decomposition features,” IEEE Geoscience and Remote Sensing Letters, vol. 12, pp. 1933-1937, 2015.## [28] P. Moallem and N. Razmjooy, “A multi layer perceptron neural network trained by invasive weed optimization for potato color image segmentation,” Trends in Applied Sciences Research, vol. 7, p. 445, 2012.## [29] N. Razmjooy, B. S. Mousavi, M. Khalilpour, and H. Hosseini, “Automatic selection and fusion of color spaces for image thresholding,” Signal, Image and Video Processing, vol. 8, pp. 603-614, 2014.## [30] E. Reddy and R. Reddy, “Dynamic clipped histogram equalization technique for enhancing low contrast images,” Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, pp. 1-26, 2018.## [31] R. A. Ghazy, A. M. Abbas, N. Al-Zubi, E. S. Hassan, N. A. El-Fishawy, M. M. Hadhoud, et al., “Block-based SVD image watermarking in spatial and transform domains,” International Journal of Electronics, vol. 102, pp. 1091-1113, 2015.## | ||
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آمار تعداد مشاهده مقاله: 393 تعداد دریافت فایل اصل مقاله: 287 |
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